The Impact of Design, Position and Combination of Aerodynamic Devices on Drag and Fuel Consumption
Bibliographic record
Abstract
The performance of several aerodynamic technologies and approaches, such as trailer skirts, trailer boat tails, gap reduction, was evaluated using track testing, model wind tunnel testing, and CFD simulation, in order to assess the influence of the design, position and combination of various aerodynamic devices. The track test procedure followed the SAE J1321 SAE Fuel Consumption Test Procedure - Type II. Scale model wind tunnel tests were conducted to have direct performance comparisons among several possible configurations. The wind tunnel tests were conducted on a 1/8 scale model of a tractor in combination with a 53-foot semi-trailer. Among others, the wind tunnel tests and CFD simulations confirmed the influences of trailer skirts' length observed during the track tests and that the wider skirt closer to the ground offer better results. The differences in the shape, dimensions and position of rear deflectors and trailer skirts on the trailer are reflected in the test results. Track testing and CFD simulation confirmed that the approach to reduce the tractor-trailer gap can result in improved fuel efficiency. The tests showed that combinations of aerodynamic devices can bring additional benefits. The results obtained by combining trailer skirts and trailer rear deflectors are better than those obtained by the trailer skirts or gap deflector alone. This can be explained by the additional reduction in aerodynamic drag at the rear of the trailer, or in the gap between the tractor and trailer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".